Dissecting virulence gene regulatory pathways in M. tuberculosis clinical isolates

Project Summary Infecting an estimated one quarter of the world’s population, M. tuberculosis (Mtb) is an ongoing threat to human health worldwide. Mtb infection can lead to a wide range of outcomes ranging from an asymptomatic latent infection to severe disease. These disease states are the function of bacterial, host, and environmental factors.

Despite ongoing selection to adapt to the host environment, the contribution of Mtb’s genetic variation is an underappreciated variable in this equation. Evolution acts on regulation of gene expression to create large phenotypic diversity from small genetic change. Our long-term objective is to understand how evolution in Mtb’s regulatory network contributes to variation in

disease outcome. In a preliminary study, we used a high-throughput transcriptomic pipeline we built to capture transcriptomes from 284 clinical isolates from Vietnam and Peru; we identified ~10% of well-transcribed genes were highly variable across isolates and linked expression outliers to mutations in transcriptional regulators or

nearby regulatory regions. Unexpectedly, we identified an up to 17-fold range in expression of ESX-1 secreted effector proteins; a key virulence pathway, ESX-1 is required for productive infection. Based on these results, we propose two aims for this study. In Aim 1, since transcriptional regulation responds to the environment, we

will capture transcriptomes from our isolate library during in vitro acid stress and hypoxia, environments from establishment of infection and survival in granuloma, respectively. By searching for convergent evolution in gene expression across the phylogenetic tree, we will identify expression programs and genetic variants that drive

adaptive change in host. In Aim 2, we will build on our discovery of ESX-1’s expression variation and integrate natural variants driving four distinct expression states across two secreted effector operons. Next, we will use these engineered strains in a macrophage and mouse model of infection to determine if and how they drive

different disease phenotypes. Mtb is under constant selective pressure to survive the host environment. This work uses Mtb’s natural variation to illuminate the genetic pathways and molecular strategies that evolution bends to this goal. This work will explore the mechanisms of Mtb genetic variation that contribute to variable disease outcomes. It is critical that

we expand our understanding of pathogens beyond a few strains: if we limit ourselves to laboratory strains, we risk designing treatments that will not work outside the laboratory.